量化投资之路２:资金流向2-如何通过资金流选股（资金流选股模型）

class IndexMoneyFlowFactor(ScoreFactor): def __init__(self, the_timestamp: Union[str, pd.Timestamp] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = [IndexMoneyFlow.net_inflows, IndexMoneyFlow.net_inflow_rate, IndexMoneyFlow.net_main_inflows, IndexMoneyFlow.net_main_inflow_rate], filters: List = None, order: object = None, limit: int = None, provider: str = sina, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = entity_id, time_field: str = timestamp, trip_timestamp: bool = True, auto_load: bool = True, keep_all_timestamp: bool = False, fill_method: str = ffill, effective_number: int = 10, scorer: Scorer = RankScorer(ascending=True)) -> None: super().__init__(IndexMoneyFlow, None, index, None, None, the_timestamp, start_timestamp, end_timestamp, columns, filters, order, limit, provider, level, category_field, time_field, trip_timestamp, auto_load, keep_all_timestamp, fill_method, effective_number, scorer) def pre_compute(self): self.depth_df = self.data_df.copy() self.depth_df = self.depth_df.groupby(level=1).rolling(window=20).mean() self.depth_df = self.depth_df.reset_index(level=0, drop=True) self.depth_df = self.depth_df.reset_index() self.depth_df = index_df_with_category_xfield(self.depth_df)

def pre_compute(self): self.depth_df = self.data_df.copy() self.depth_df = self.depth_df.groupby(level=1).rolling(window=20).mean() self.depth_df = self.depth_df.reset_index(level=0, drop=True) self.depth_df = self.depth_df.reset_index() self.depth_df = index_df_with_category_xfield(self.depth_df)

class RankScorer(Scorer): def __init__(self, ascending=True) -> None: self.ascending = ascending def compute(self, input_df) -> pd.DataFrame: result_df = input_df.groupby(level=1).rank(ascending=self.ascending, pct=True) return result_df

class IndexSelector(TargetSelector): def __init__(self, entity_ids=None, entity_type=stock, exchanges=[sh, sz], codes=None, the_timestamp=None, start_timestamp=None, end_timestamp=None, long_threshold=0.8, short_threshold=0.2, level=IntervalLevel.LEVEL_1DAY, provider=sina, block_selector=None) -> None: super().__init__(entity_ids, entity_type, exchanges, codes, the_timestamp, start_timestamp, end_timestamp, long_threshold, short_threshold, level, provider, block_selector) def init_factors(self, entity_ids, entity_type, exchanges, codes, the_timestamp, start_timestamp, end_timestamp, level): index_factor = IndexMoneyFlowFactor(start_timestamp=start_timestamp, end_timestamp=end_timestamp, level=level, provider=sina, codes=codes) self.score_factors.append(index_factor)

In [1]: from examples.factors.composite_selector import * from zvt.api import * In [2]: df = get_blocks(provider=sina, block_category=industry) index_selector = IndexSelector(entity_type=index, exchanges=None, start_timestamp=2019-01-01, end_timestamp=now_pd_timestamp(), codes=df[code].to_list()) index_selector.run()

In [12]: blocks = index_selector.get_targets(timestamp=2019-09-02) In [13]: get_index(entity_ids=blocks,provider=sina) Out[13]: id entity_id timestamp entity_type exchange code name is_delisted category base_point list_date timestamp NaT index_cn_new_jdhy index_cn_new_jdhy None index cn new_jdhy 家电行业 None industry None None NaT index_cn_new_jdly index_cn_new_jdly None index cn new_jdly 酒店旅游 None industry None None NaT index_cn_new_jjhy index_cn_new_jjhy None index cn new_jjhy 家具行业 None industry None None NaT index_cn_new_jrhy index_cn_new_jrhy None index cn new_jrhy 金融行业 None industry None None NaT index_cn_new_jtys index_cn_new_jtys None index cn new_jtys 交通运输 None industry None None NaT index_cn_new_jxhy index_cn_new_jxhy None index cn new_jxhy 机械行业 None industry None None

In [14]: get_securities_in_blocks(provider=sina,categories=[industry],codes=[new_jdly]) Out[14]: [stock_sh_600054, stock_sh_600138, stock_sh_600258, stock_sh_600358, stock_sh_600555, stock_sh_600593, stock_sh_600640, stock_sh_600706, stock_sh_600749, stock_sh_600754, stock_sh_601007, stock_sh_601888, stock_sh_603099, stock_sh_603199, stock_sh_603869, stock_sz_000007, stock_sz_000069, stock_sz_000428, stock_sz_000430, stock_sz_000524, stock_sz_000610, stock_sz_000613, stock_sz_000721, stock_sz_000802, stock_sz_000888, stock_sz_000978, stock_sz_002033, stock_sz_002059, stock_sz_002159, stock_sz_002186, stock_sz_002306, stock_sz_002558, stock_sz_002707, stock_sz_300144, stock_sz_300178]

class TechnicalSelector(TargetSelector): def __init__(self, entity_ids=None, entity_type=stock, exchanges=[sh, sz], codes=None, the_timestamp=None, start_timestamp=None, end_timestamp=None, long_threshold=0.8, short_threshold=0.2, level=IntervalLevel.LEVEL_1DAY, provider=joinquant, block_selector=None) -> None: super().__init__(entity_ids, entity_type, exchanges, codes, the_timestamp, start_timestamp, end_timestamp, long_threshold, short_threshold, level, provider, block_selector) def init_factors(self, entity_ids, entity_type, exchanges, codes, the_timestamp, start_timestamp, end_timestamp, level): bull_factor = BullFactor(entity_ids=entity_ids, entity_type=entity_type, exchanges=exchanges, codes=codes, the_timestamp=the_timestamp, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider=joinquant, level=level) self.add_filter_factor(bull_factor)

s = TechnicalSelector(codes=SAMPLE_STOCK_CODES, start_timestamp=2019-01-01, end_timestamp=2019-06-30, block_selector=index_selector) s.run() s.draw()